A new method of wavelet transform-based edge detection
In many edge detection methods, Finding a proper threshold is an unavoidable step. In this paper, a new algorithm of edge detection is proposed based on wavelet transform. After multiplying the DWT coefficients in the adjacent scale, a new method is proposed to calculate the proper threshold, which...
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| creator | Tao Yang Guoxia Sun Xiuman Duan |
| description | In many edge detection methods, Finding a proper threshold is an unavoidable step. In this paper, a new algorithm of edge detection is proposed based on wavelet transform. After multiplying the DWT coefficients in the adjacent scale, a new method is proposed to calculate the proper threshold, which is used to separate the coefficients come from wavelet transform. After the multiplying the coefficients in adjacent scale, the product coming from noise are small and accounts for the most part of the data, while there are less product, whose amplitudes are bigger, coming from edge. Thus, we statistics the product and get the interval in which the amount of the product is the biggest. The threshold is the upper bound of the interval. A scheme is then designed to synthesis the two edge maps obtained in two orthometric directions. A set of the experiments demonstrate the effective of the approach. |
| doi_str_mv | 10.1109/ICCT.2011.6157985 |
| format | Conference Proceeding |
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In this paper, a new algorithm of edge detection is proposed based on wavelet transform. After multiplying the DWT coefficients in the adjacent scale, a new method is proposed to calculate the proper threshold, which is used to separate the coefficients come from wavelet transform. After the multiplying the coefficients in adjacent scale, the product coming from noise are small and accounts for the most part of the data, while there are less product, whose amplitudes are bigger, coming from edge. Thus, we statistics the product and get the interval in which the amount of the product is the biggest. The threshold is the upper bound of the interval. A scheme is then designed to synthesis the two edge maps obtained in two orthometric directions. A set of the experiments demonstrate the effective of the approach.</description><identifier>ISBN: 1612843069</identifier><identifier>ISBN: 9781612843063</identifier><identifier>EISBN: 1612843050</identifier><identifier>EISBN: 9781612843070</identifier><identifier>EISBN: 9781612843056</identifier><identifier>EISBN: 1612843077</identifier><identifier>DOI: 10.1109/ICCT.2011.6157985</identifier><language>eng</language><publisher>IEEE</publisher><subject>Additives ; Discrete wavelet transforms ; Edge detection ; Histograms ; Image edge detection ; Image processing ; Noise ; Statistics histogram ; Threshold ; Wavelet transform</subject><ispartof>2011 IEEE 13th International Conference on Communication Technology, 2011, p.789-792</ispartof><woscitedreferencessubscribed>false</woscitedreferencessubscribed></display><links><openurl>$$Topenurl_article</openurl><openurlfulltext>$$Topenurlfull_article</openurlfulltext><thumbnail>$$Tsyndetics_thumb_exl</thumbnail><linktohtml>$$Uhttps://ieeexplore.ieee.org/document/6157985$$EHTML$$P50$$Gieee$$H</linktohtml><link.rule.ids>309,310,776,780,785,786,2051,27904,54899</link.rule.ids><linktorsrc>$$Uhttps://ieeexplore.ieee.org/document/6157985$$EView_record_in_IEEE$$FView_record_in_$$GIEEE</linktorsrc></links><search><creatorcontrib>Tao Yang</creatorcontrib><creatorcontrib>Guoxia Sun</creatorcontrib><creatorcontrib>Xiuman Duan</creatorcontrib><title>A new method of wavelet transform-based edge detection</title><title>2011 IEEE 13th International Conference on Communication Technology</title><addtitle>ICCT</addtitle><description>In many edge detection methods, Finding a proper threshold is an unavoidable step. 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A set of the experiments demonstrate the effective of the approach.</description><subject>Additives</subject><subject>Discrete wavelet transforms</subject><subject>Edge detection</subject><subject>Histograms</subject><subject>Image edge detection</subject><subject>Image processing</subject><subject>Noise</subject><subject>Statistics histogram</subject><subject>Threshold</subject><subject>Wavelet transform</subject><isbn>1612843069</isbn><isbn>9781612843063</isbn><isbn>1612843050</isbn><isbn>9781612843070</isbn><isbn>9781612843056</isbn><isbn>1612843077</isbn><fulltext>true</fulltext><rsrctype>conference_proceeding</rsrctype><creationdate>2011</creationdate><recordtype>conference_proceeding</recordtype><sourceid>6IE</sourceid><sourceid>RIE</sourceid><recordid>eNpFj81KAzEURiMiqLUPIG7yAjPmJs1NsiyDP4WCC7svd5IbHenMyEyw-PYKFvw2h7M58AlxC6oGUOF-0zS7WiuAGsG64O2ZuAYE7VdGWXX-LxguxXKeP9TvEH0AeyVwLQc-yp7L-5jkmOWRvvjARZaJhjmPU1-1NHOSnN5YJi4cSzcON-Ii02Hm5YkL8fr4sGueq-3L06ZZb6suqFL5bLQncK03qNiTazFRcj67DBRUm7zROUZlIplAHjHGRNo7m1e2xWAW4u6v2jHz_nPqepq-96eX5gcWGkW6</recordid><startdate>201109</startdate><enddate>201109</enddate><creator>Tao Yang</creator><creator>Guoxia Sun</creator><creator>Xiuman Duan</creator><general>IEEE</general><scope>6IE</scope><scope>6IL</scope><scope>CBEJK</scope><scope>RIE</scope><scope>RIL</scope></search><sort><creationdate>201109</creationdate><title>A new method of wavelet transform-based edge detection</title><author>Tao Yang ; Guoxia Sun ; Xiuman Duan</author></sort><facets><frbrtype>5</frbrtype><frbrgroupid>cdi_FETCH-LOGICAL-i90t-8f328a17b8360e8a7b6dad78f7f1a90bd832fcc03ca39a866ccda2875f45b693</frbrgroupid><rsrctype>conference_proceedings</rsrctype><prefilter>conference_proceedings</prefilter><language>eng</language><creationdate>2011</creationdate><topic>Additives</topic><topic>Discrete wavelet transforms</topic><topic>Edge detection</topic><topic>Histograms</topic><topic>Image edge detection</topic><topic>Image processing</topic><topic>Noise</topic><topic>Statistics histogram</topic><topic>Threshold</topic><topic>Wavelet transform</topic><toplevel>online_resources</toplevel><creatorcontrib>Tao Yang</creatorcontrib><creatorcontrib>Guoxia Sun</creatorcontrib><creatorcontrib>Xiuman Duan</creatorcontrib><collection>IEEE Electronic Library (IEL) Conference Proceedings</collection><collection>IEEE Proceedings Order Plan All Online (POP All Online) 1998-present by volume</collection><collection>IEEE Xplore All Conference Proceedings</collection><collection>IEEE Electronic Library (IEL)</collection><collection>IEEE Proceedings Order Plans (POP All) 1998-Present</collection></facets><delivery><delcategory>Remote Search Resource</delcategory><fulltext>fulltext_linktorsrc</fulltext></delivery><addata><au>Tao Yang</au><au>Guoxia Sun</au><au>Xiuman Duan</au><format>book</format><genre>proceeding</genre><ristype>CONF</ristype><atitle>A new method of wavelet transform-based edge detection</atitle><btitle>2011 IEEE 13th International Conference on Communication Technology</btitle><stitle>ICCT</stitle><date>2011-09</date><risdate>2011</risdate><spage>789</spage><epage>792</epage><pages>789-792</pages><isbn>1612843069</isbn><isbn>9781612843063</isbn><eisbn>1612843050</eisbn><eisbn>9781612843070</eisbn><eisbn>9781612843056</eisbn><eisbn>1612843077</eisbn><abstract>In many edge detection methods, Finding a proper threshold is an unavoidable step. In this paper, a new algorithm of edge detection is proposed based on wavelet transform. After multiplying the DWT coefficients in the adjacent scale, a new method is proposed to calculate the proper threshold, which is used to separate the coefficients come from wavelet transform. After the multiplying the coefficients in adjacent scale, the product coming from noise are small and accounts for the most part of the data, while there are less product, whose amplitudes are bigger, coming from edge. Thus, we statistics the product and get the interval in which the amount of the product is the biggest. The threshold is the upper bound of the interval. A scheme is then designed to synthesis the two edge maps obtained in two orthometric directions. A set of the experiments demonstrate the effective of the approach.</abstract><pub>IEEE</pub><doi>10.1109/ICCT.2011.6157985</doi><tpages>4</tpages></addata></record> |
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| source | IEEE Electronic Library (IEL) Conference Proceedings |
| subjects | Additives Discrete wavelet transforms Edge detection Histograms Image edge detection Image processing Noise Statistics histogram Threshold Wavelet transform |
| title | A new method of wavelet transform-based edge detection |
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